Abstract

Pharmacogenomics aims to investigate the genetic basis of inter-individual differences in drug responses, such as efficacy, dose requirements and adverse events. Research in pharmacogenomics has grown over the past decade, evolving from a candidate-gene approach to genome-wide association studies (GWASs). Genetic variants in genes coding for drug metabolism, drug transport and more recently human-leukocyte antigens (HLAs) have been linked to inter-individual differences in the risk of adverse drug reactions (ADRs). The tight association of specific HLA alleles with Stevens–Johnson syndrome, toxic epidermal necrolysis, drug hypersensitivity syndrome and drug-induced liver injury underscore the importance of HLA in the pathogenesis of these idiosyncratic drug hypersensitivity reactions. However, as with the search for the genetic basis for common diseases, pharmacogenomic research, including GWAS, has so far been a disappointment in discovering major gene variants responsible for the efficacy of drugs used to treat common diseases. This review focuses on the pharmacogenomics of ADRs, the underlying mechanisms and the potential use of genomic biomarkers in clinical practice for dose adjustment and the avoidance of drug toxicity. We also discuss obstacles to the implementation of pharmacogenomics and the direction of future translational research.

INTRODUCTION

Pharmacogenetics is an area of research that addresses the genetically determined variation in how individuals respond to specific drugs, in terms of differences in dose requirement, efficacy and the risk of adverse drug reactions (ADRs). Pharmacogenomics, in addition to addressing variability in DNA, is also concerned with gene expression profiling. In line with the increasing use of functional genomics, pharmacogenetics and pharmacogenomics have been used interchangeably (1,2).

Genetically determined variations affecting inter-individual responses to drugs can be grouped, in a broad sense, into germ-line genetic variants and somatic mutations as occur in tumor tissues. Germ-line genetic variants—mainly in genes encoding drug-metabolizing enzymes, drug transporters, drug targets and human-leukocyte antigen (HLA)—are reported to be responsible for many of the observed inter-individual differences in drug efficacy, the risk for ADRs, or both. The different somatic mutations in cancer have allowed the development of new anti-cancer agents aimed at treating patients whose cancer carries the targeted mutations, so-called targeted therapies. The pharmacogenetics of targeted anti-cancer therapy has been extensively reviewed recently (3).

Since the completion of the Human Genome Project, pharmacogenomics has been touted as the field with greatest clinical potential to radically improve patient care through the implementation of personalized medicine. The terms personalized medicine and pharmacogenomics are often used together, as both aim to maximize therapeutic benefit and avoid ADRs. In addition to improving patient care, pharmacogenetics-based personalized approaches have the potential to save money by improving the cost-effectiveness of health care delivery. There are many commonly prescribed drugs that fail to work for some patients. For example, many patients with high cholesterol fail to respond to statins, and many hypertensive patients do not respond to beta-blockers (4). The ability to prescribe drugs only to individuals identified as responders would significantly reduce wasted medical costs. Furthermore, by not prescribing drugs to those genetically at risk for ADRs, the costs associated with caring for patients with untoward drug toxicities could be eliminated.

ADRs are a major clinical problem that accounts for 6.7% of all hospitalizations and ranks between the fourth and sixth most common cause of inpatient death in western countries, posing challenges to the healthcare system in terms of both patient wellbeing and medical costs (5,6). ADRs are also a major burden for the pharmaceutical industry. From 1990 to 2012, there have been 43 drugs withdraw from the market due to severe ADRs (7). ADRs are often classified into two groups. Type A reactions are predictable by the mode of pharmacological mechanisms and are often dose-dependent. In contrast, type B reactions, which account for ∼15% of ADRs, are historically referred to as unpredictable, dose-independent, idiosyncratic reactions (8,9).

Recent pharmacogenomic studies that have evolved from a candidate-gene approach to the genome-wide association study (GWAS) have greatly advanced the discovery of genes associated with inter-individual differences in drug response, especially genes that predispose individuals to ADRs and, to a lesser extent, genes responsible for drug efficacy. These studies also have advanced our understanding of the underlying mechanisms of ADRs and drug efficacy. Based on these discoveries, the Food and Drug Administration (FDA) has relabeled over 100 approved drugs to include genetic information. A list of valid genomic biomarkers for clinical guidance can be found on the FDA website ‘Table of Pharmacogenomic Biomarkers in Drug labels’ (http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm). Here, we summarize recent important findings that advance our knowledge of the genetic contribution to inter-individual variability in drug efficacy and ADRs. We focus on the pharmacogenomics of ADRs, especially on genes coding for drug metabolizing enzymes, drug transporters and HLAs, as well as the potential use of these genomic biomarkers in clinical practice for dose adjustment and for preventing drug toxicity. We also discuss key challenges for the implementation of pharmacogenomics and the direction for future translational research.

DRUG-METABOLIZING ENZYMES

Before genome-wide technologies were available, early pharmacogenomic studies relied on candidate-gene approaches; thus, genes affecting drug metabolism and detoxification were obvious candidates. As a result, numerous metabolic biomarkers have been identified (Fig. 1). As of July 2012, 67 drugs with valid metabolic biomarkers for dosage adjustment have been listed in the Table of Pharmacogenomic Biomarkers in Drug Labels; of these, 87% have genetic tests approved or cleared by the FDA. However, for most there are no guidelines to direct the clinical use of this genetic information (10). Among these drugs, ∼25% are metabolized by cytochrome P450, family 2, subfamily D, polypeptide 6 (CYP2D6) and their rates of metabolism can vary >100-fold depending on allelic variability in different ethnic groups (11). Seven percent of Western Europeans are CYP2D6 poor metabolizers who require lower prescribing doses, whereas an estimated 20 million individuals are ultra-rapid metabolizers who experience no response to standard treatment (12). For example, one meta-analysis demonstrated a reduction in ∼50% in the average dose for most tricyclic antidepressants in patients who are CYP2D6 poor metabolizers (CYP2D6*3/*3) (13). In the case of codeine, which requires CYP2D6 for bioactivation and conversion to morphine, poor metabolizers experience little therapeutic effect, whereas morphine conversion is increased in ultra-rapid metabolizers (CYP2D6*1/*1 and *1/*2), which results in severe or life threatening toxic side effects following standard doses (14).

Figure 1.

Metabolic enzymes identified in drug labels of FDA-approved drugs. The percentage of drugs with genetic information on responding metabolic enzymes in their drug labels related to dosage adjustment or risk for adverse events is shown.

Figure 1.

Metabolic enzymes identified in drug labels of FDA-approved drugs. The percentage of drugs with genetic information on responding metabolic enzymes in their drug labels related to dosage adjustment or risk for adverse events is shown.

Clopidogrel is a thienopyridine antiplatelet drug used to prevent recurrent thrombosis in patients with myocardial infarction and percutaneous coronary intervention with stent implantation. However, responses to clopidogrel vary widely, both inter-individually and inter-ethnically. Several genes have been investigated, but variations in the CYP2C19 gene appear to be the most consistent genetic determinants for differences in response to clopidogrel treatment. Patients who carry the reduced function alleles CYP2C19*2 are at higher risk for major cardiovascular events compared with non-carriers (15).

Another important drug-metabolizing enzyme is thiopurine S-methyltransferase (TPMT), which metabolizes 6-merceptopurine and azathioprine (16). TPMT-deficient patients carrying the non-functional alleles TPMT*2, TPMT*3A and TPMT*3C are at high risk of severe hematologic toxicity, and homozygous-TPMT-deficient patients require substantial dose reductions. Reliable TPMT genotyping tests with high sensitivity (90%) and specificity (99%) are commercially available and allow proper dose adjustment (17). Similarly, patients with a polymorphism that results in decreased expression of uridine diphospho glucuronosyltransferase 1A1 (UGT1A1) are at a risk for neutropenia following the initiation of irinotecan treatment (18). The homozygous and heterozygous genotypes of UGT1A1*28 present the most significant risk, and a reduced initial dose of irinotecan is suggested for these patients.

In addition to the metabolizing enzymes that affect drug pharmacokinetics, there are genetic variants that influence drug pharmacodynamics. One successful example of a drug for which both pharmacokinetic and pharmacodynamic biomarkers are used for individualized dose prediction is warfarin. Warfarin is the most commonly prescribed anticoagulant. Despite its clinical effectiveness, warfarin has a narrow therapeutic index and shows large inter-individual variability (19). Warfarin overdose is often associated with major bleeding complications (20). Both candidate-gene and GWA studies have confirmed that dose requirement of warfarin is primarily determined by CYP2C9, coding for the enzyme that metabolizes the potent S-isomer of warfarin, and vitamin K epoxide reductase enzyme complex subunit 1 (VKORC1), encoding the warfarin target protein (21–24). It is now recognized that compared with wild-type CYP2C9*1, the non-synonymous polymorphisms CYP2C9*2 and *3 coding variants with reduced enzymatic activity and prolonged warfarin half-life have a significant clinical influence on warfarin sensitivity and severe bleeding events. On the other hand, the non-coding polymorphism of VKORC1 at the promoter region, a guanine to adenine substitution (G→A) at position −1639, decreases expression of the gene and the availability of vitamin K. Recently, a large collaborative study with multi-ethnic groups, the International Warfarin Pharmacogenetics Consortium, established a warfarin dosing algorithm that incorporates the clinical factors and genotypes of CYP2C9 and VKORC1 to more accurately predict warfarin doses (25,26). A large prospective randomized multicenter double-blinded study comparing the genotype guided dosing of warfarin with other approaches is ongoing (http://clinicaltrials.gov/ct2/show/NCT01124058).

ENZYMES IN INBORN ERRORS OF METABOLISM

Enzymes affecting drug metabolism can also be found in two classical inborn errors of metabolism, dihydropyrimidine dehydrogenase (DPD) deficiency and glucose-6-phosphate dehydrogenase (G6PD) deficiency. DPD is the rate-limiting enzyme involved in the catabolism of thymidine and uracil. It is also the main enzyme involved in the degradation of structurally related compounds like 5-fluorouracil (5-FU) or its prodrug capecitabine, two widely used anticancer drugs. A decrease in DPD activity can result in toxicity to 5-FU and capecitabine; therefore, these drugs should not be used in DPD-deficient patients (27,28). G6PD deficiency is characterized by abnormally low levels of G6PD, a metabolic enzyme involved in the pentose phosphate pathway. The most notable symptom of G6PD deficiency is hemolytic anemia caused by ingestion of drugs, food and other trigger substances that cause oxidative stress. Of the many drugs known to cause hemolytic anemia in patients with G6PD deficiency, chloroquine, dapsone and rasburicase are the three for which the FDA recommends screening for G6PD deficiency before beginning treatment. Rasburicase is a recombinant uricase recently approved for the management of high uric acid levels associated with chemotherapy for certain type of cancer. Patients deficient in G6PD have an impaired ability to reduce hydrogen peroxide formed as a major byproduct of the rasburicase-catalyzed oxidation of uric acid to allantoin.

DRUG TRANSPORTERS

Drug transporters represent another class of genes affecting drug pharmacokinetics. These are mainly classified into two major superfamilies: the efflux transporter ATP-binding cassette (ABC) and the influx transporter solute carrier (SLC) transporters (29). For instance, genetic variants of ABCB1, encoding p-glycoprotein (Pgp) associated with multiple drug resistance, may account for a difference of 25% in the renal clearance of cyclosporine (30). In fact, the functional polymorphism ABCB1 34355TT is strongly associated with cyclosporine-induced nephrotoxicity (31). Similarly, subjects with Q141K variant of ABCG2, which codes for breast cancer resistance protein, are at risk of gefitinib-induced diarrhea (32).

Statins, or HMG-CoA reductase inhibitors, are one of the most commonly prescribed classes of drug for reducing cholesterol levels and preventing cardiovascular events (33). However, patients treated with a statin are at risk for muscle complications, including myopathy or fatal rhabdomyolysis. A recent GWAS study identified a strong association between simvastatin-induced myopathy and the SLC organic anion transporter family member 1B1 (SLCO1B1), which encodes the organic anion-transporting polypeptide (OATP1B1). Homozygous CC of the SNP rs4363657 accounts for an 18% cumulative risk of myopathy (34). In addition, clinical studies have shown that the C allele of rs4149056 SLCO1B1 is also associated with higher blood statin concentrations and increased risk of myopathy (35). However, the association of rs4149056 in SLCO1B1 with simvastatin-induced myopathy is not highly predictive for other statins, suggesting that this association may not be a class effect (36,37). Consequently, genotyping of SLCO1B1 may be a clinically useful tool for personalized dose regulation in preventing simvastatin-induced myopathy (38).

HUMAN LEUKOCYTE ANTIGENS

The HLA system has been a major focus for Type B ADRs, i.e. those associated with drug hypersensitivity reactions, including Stevens–Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), hypersensitivity syndrome (HSS) and drug-induced liver injury. Ample evidence supports the view that drug hypersensitivity is mediated by adaptive immunity, which involves MHC-restricted drug presentation, activation and clonal expansion of T cells. The specific MHC molecules involved have been identified, for example, HLA-B*5701 in abacavir-induced drug hypersensitivity and HLA-B*1502 in carbamazepine (CBZ)-induced SJS (see Table 1 for the list of ADRs with HLA association).

Table 1.

Serious adverse drug reactions with HLA association

Drug HLA allele Severe ADR Ethnicity OR Reference 
Abacavir HLA-B*5701 HSS Western Australian 117 (57
Allopurinol HLA-B*5801 SCAR Han Chinese 580 (58
SJS/TEN Japanese 41 (59
SJS/TEN European 80 (60
Aminopenicillin HLA-A2 DHS Italian (61
HLA-DRW52 DHS Italian (13
Amoxicillin-clavulanate HLA-A*0201 DILI Caucasian 2.3 (62
DRB1*1501-DQB1*0602 DILI  2.8 (62
Aspirin HLA-DRB1*1302-DQB1*0609 Urticaria Korean (40
Carbamazepine HLA-B*1502 SJS/TEN Han Chinese 2504 (63
HLA-A*3101 MPE Han Chinese 17 (39
 SCAR Japanese 11 (42
 cADR Northern European (43
Clozapine HLA-DRB5*0201 Agranulocytosis Caucasian 22 (64
Flucloxacillin HLA-B*5701 DILI Caucasian 81 (65
Lamotrigine HLA-B*3801 SJS/TEN European 32 (60
Lumiracoxiba HLA-DRB1*1501-DQB1*0602-DRB5*0101 DILI Multiple populations (66
Methazolamide HLA-B*5901 SJS/TEN Korean 250 (67
Nevirapine HLA-Cw8-B14 DHS Italian Sardinian 15 (68
HLA-Cw8 DHS Japanese 6.2 (69
HLA-B*3505 DHS Thai 49 (70
HLA-DRB1*0101 DHS Western Australian 18 (71
Oxicams HLA-B*7301 SJS/TEN European 152 (60
Phenytoin HLA-B*1502 SJS/TEN Thai 36 (72
Sulfomethoxazole HLA-B*3802 SJS/TEN European 76 (60
Ximelagatrana HLA-DRB1*0701 DILI Northern European (73
Drug HLA allele Severe ADR Ethnicity OR Reference 
Abacavir HLA-B*5701 HSS Western Australian 117 (57
Allopurinol HLA-B*5801 SCAR Han Chinese 580 (58
SJS/TEN Japanese 41 (59
SJS/TEN European 80 (60
Aminopenicillin HLA-A2 DHS Italian (61
HLA-DRW52 DHS Italian (13
Amoxicillin-clavulanate HLA-A*0201 DILI Caucasian 2.3 (62
DRB1*1501-DQB1*0602 DILI  2.8 (62
Aspirin HLA-DRB1*1302-DQB1*0609 Urticaria Korean (40
Carbamazepine HLA-B*1502 SJS/TEN Han Chinese 2504 (63
HLA-A*3101 MPE Han Chinese 17 (39
 SCAR Japanese 11 (42
 cADR Northern European (43
Clozapine HLA-DRB5*0201 Agranulocytosis Caucasian 22 (64
Flucloxacillin HLA-B*5701 DILI Caucasian 81 (65
Lamotrigine HLA-B*3801 SJS/TEN European 32 (60
Lumiracoxiba HLA-DRB1*1501-DQB1*0602-DRB5*0101 DILI Multiple populations (66
Methazolamide HLA-B*5901 SJS/TEN Korean 250 (67
Nevirapine HLA-Cw8-B14 DHS Italian Sardinian 15 (68
HLA-Cw8 DHS Japanese 6.2 (69
HLA-B*3505 DHS Thai 49 (70
HLA-DRB1*0101 DHS Western Australian 18 (71
Oxicams HLA-B*7301 SJS/TEN European 152 (60
Phenytoin HLA-B*1502 SJS/TEN Thai 36 (72
Sulfomethoxazole HLA-B*3802 SJS/TEN European 76 (60
Ximelagatrana HLA-DRB1*0701 DILI Northern European (73

cADR, cutaneous adverse drug reaction; DHS, delayed-type hypersensitivity reaction; DILI, drug-induced liver injury; HSS, hypersensitivity syndrome; SCAR, severe cutaneous adverse drug reaction; SJS, Stevens–Johnson syndrome; TEN, toxic epidermal necrolysis; NA, not available; OR, odds ratio.

aWithdrawn from the markets.

The HLA/ADR association is known to be phenotype specific. In the case of CBZ-induced cutaneous ADRs, studies in Han Chinese demonstrate that CBZ-SJS/TEN is highly associated with HLA-B*1502, whereas CBZ-induced maculopapular eruption and HSS are not. Instead, induced maculopapular eruption is associated with SNPs in the HLA-E region and HLA-A*3101, and HSS is associated with the MHC class II genes (39). Likewise, HLA-DPB1*0301 is related to aspirin-induced asthma, while HLA-DRB1*1302 and HLA-DQB1*0609 are associated with aspirin-induced urticaria/angioedema and asthma (40,41). The discrepancy of HLA association in hypersensitivities induced by the same drug may contribute to distinct pathogenesis of particular disease phenotypes. It should be noted that the HLA association in CBZ-induced cutaneous ADRs seen in Japanese and Caucasian patients does not show phenotypic specificity (42,43).

It is also widely recognized that the genetic association can also be ethnicity specific, which could be due to difference in allele frequency. In populations such as Japanese and Caucasians, where the HLA-B*1502 allele is very low to absent, the susceptibility to CBZ-SJS is not associated with HLA-B*1502. It is instead associated with HLA-A*3101, which is present at a higher allelic frequency in Japanese (9.1%) and Caucasians (5%), but is found in only 1.8% of Han-Chinese (http://www.allelefrequencies.net/). Similarly, there is also an ethnic difference in the genetic association between hypersensitivity induced by abacavir and HLA-B*5701, which is prevalent in Caucasians, but not in Hispanics or Africans (44). These studies illustrate that ancestry plays an important role in the biomarker assessment of drug hypersensitivity.

The physiological role of HLA is to present an antigen to the T cell receptor, thereby initiating the T cell-mediated immune response (45). The strong association between HLA alleles and ADRs implies a causal relationship of HLA in the development of drug hypersensitivity, with the offending drug in the role of antigen. In support of this view, drug-specific CD8+ cytotoxic T cells activated in a HLA class I-restricted pathway were found in the blister fluid of drug-induced SJS/TEN patients (46). Currently, there are two drug-presentation hypotheses, the hapten concept and p–i concept (the direct pharmacological interaction of a drug with immune receptors) (Fig. 2). According to the hapten concept, chemically reactive drugs or metabolites covalently bind a protein or peptide to become neo-epitopes (47). An example is the covalent binding of penicillin to lysine residue of serum albumin and its presentation by HLA through the classical processing-required pathway to trigger T cell activation, eliciting penicillin allergy (48). Conversely, the p–i concept proposes a direct interaction between drugs and immune receptors, such as the T-cell receptor or HLA (49). For example, CBZ interacts directly with HLA-B*1502 without drug-modified peptide formation, which is sufficient to elicit cytotoxic T lymphocyte activation (50,51). Key interacting chemical moieties on CBZ and residues in the HLA-B*1502 antigen-binding cleft have also been identified to explain the specificity of HLA/drug by steric complementarity and non-covalent interacting forces (e.g. hydrogen bonding).

Figure 2.

Working model of severe drug hypersensitivity reactions. (Upper panel) A schematic diagram of the hapten concept. Most drugs are small molecules and are unlikely to trigger an immune reaction on their own, so the specific drugs or metabolites act as haptens that bind covalently to endogenous proteins and form distinct antigenic epitopes. The haptenized peptides present on MHC after cellular processing and are recognized by T cells for HLA-restricted T cell activation. (Lower panel) A schematic diagram of p–i concept (pharmacologic interaction with immune receptors). The chemically inert drug can bind directly to peptide/HLA complexes without cellular processing to activate drug-specific T cells.

Figure 2.

Working model of severe drug hypersensitivity reactions. (Upper panel) A schematic diagram of the hapten concept. Most drugs are small molecules and are unlikely to trigger an immune reaction on their own, so the specific drugs or metabolites act as haptens that bind covalently to endogenous proteins and form distinct antigenic epitopes. The haptenized peptides present on MHC after cellular processing and are recognized by T cells for HLA-restricted T cell activation. (Lower panel) A schematic diagram of p–i concept (pharmacologic interaction with immune receptors). The chemically inert drug can bind directly to peptide/HLA complexes without cellular processing to activate drug-specific T cells.

The tight HLA association in certain drug-induced hypersensitivity reactions (odds ratio >100, Table 1) provides a plausible basis for further development of such a test to identify individuals at risk of developing these life-threatening conditions. In fact, the FDA has recommended HLA-B*1502 genetic screening before prescribing CBZ to reduce the risk of SJS and TEN and HLA-B*5701 testing to avoid abacavir-induced hypersensitivity, in patients with ancestry from areas in which those HLA-B alleles are prevalent. Recent prospective studies using HLA genotyping as a screening tool before abacavir or CBZ treatment have illustrated the remarkable capability of HLA screening to prevent these severe ADRs, indicating that personalized medicine and pharmacogenomics are extremely useful in the right clinical setting. These studies have made genetic testing to prevent drug toxicity a clinical reality.

TRANSLATING PHARMACOGENOMICS FINDINGS INTO CLINIC

Table 2 lists some pharmacogenomic tests for drugs currently in use that have practical value in predicting ADRs and/or drug efficacy. These are based on well-defined genetic variants that are known to have reproducible and significant consequences for drug therapy. These tests have high predictive values (either high negative predictive value, high positive predictive value or both), and a causal relationship between genetic variations and drug response and clinical utility have been established. Many of the tests also have clinical guidelines for dose adjustment and alternative medications assembled by The Clinical Pharmacogenomics Implementation Consortium (Table 2). The biomarkers include the genetic variants in the above-mentioned drug metabolizing enzymes, inborn errors of metabolism, drug transporters and HLA alleles. The tests are available commercially as well as in academic settings. In addition, the costs of the tests may be reimbursed by third-party payers, for example, Taiwan National Health Insurance pays for the HLA-B*1502 test for all new CBZ users, some private insurance companies in the USA and Australia pay for the HLA-B*5701 test for abacavir users, and more recently, US Medicare pays for the CYP2C19 test for clopidogrel treatment. The tests are also available as an FDA-approved panel, including a pharmacogenetic test that covers all gene variants of CYP2D6 and CYP2C19 (Roche Amplichip CYP450 Test). However, the implementation of the vast information generated from the chip is still problematic. CYP2D6 metabolizes more than 100 commercially available drugs; with the exception of codeine and doxepin, the need for dose adjustment for these drugs is unclear. Thus, further research is required on how to best use the information from these gene chips.

Table 2.

Clinical useful pharmacogenomics tests in predicting drug efficacy and adverse drug reactions

Biomarkers Drugs Clinical application 
CYP2C9 Celecoxib Consider starting treatment at half the lowest recommended dose in poor metabolizers (CYP2C9*3/*3) to avoid adverse cardiovascular and gastrointestinal events 
Flurbiprofen Poor metabolizers (CYP2C9*3/*3) should administrated with caution to avoid adverse cardiovascular and gastrointestinal events 
CYP2C9+VKORC1 Warfarina Dose adjustment based on CYP2C9 and VKORC1 genotypes to achieve efficacy and avoid bleeding complications 
CYP2C19 Clopidogrela Poor metabolizers (CYP2C19*2/*2) should take alternative therapy to avoid bleeding complications 
CYP2D6 Codeinea Ultra-rapid metabolizers (CYP2D6*1/*1 and *1/*2) should avoid usage due to potential for toxicity 
Doxepin Poor metabolizers (CYP2D6*3/*3) should reduce dose by 60% to avoid arrhythmia and myelosuppression 
DPD deficiency Capecitabine Avoid usage in DPD deficient patients to prevent severe ADRs 
Fluorouracila 
G6PD deficiency Chloroquine Avoid usage in G6PD deficient patients to prevent hemolysis 
Dapsone 
Rasburicase 
HLA-B*1502 Carbamazepinea Avoid usage in HLA-B*1502 carriers to prevent SJS/TEN 
Phenytoin 
HLA-B*5701 Abacavira Avoid usage in HLA-B*5701 carriers to prevent hepatotoxicity 
Flucloxacillin 
HLA-B*5801 Allopurinol Avoid usage in HLA-B*5801 carriers to prevent severe cutaneous ADRs 
SLCO1B1 Simvastatina Dose adjustment based on SLCO1B1 genotype (C allele of rs4149056 SLCO1B1) to avoid myopathy 
TPMT Azathioprinea Dose adjustment based on TPMT genotype to achieve efficacy and avoid bone-marrow suppression (non-functional alleles TPMT*2, TPMT*3A, and TPMT*3C) 
Mercaptopurinea 
UGT1A1 Irinotecana Dose adjustment based on UGT1A1 genotype (UGT1A1*28) to achieve efficacy and avoid neutropenia 
Biomarkers Drugs Clinical application 
CYP2C9 Celecoxib Consider starting treatment at half the lowest recommended dose in poor metabolizers (CYP2C9*3/*3) to avoid adverse cardiovascular and gastrointestinal events 
Flurbiprofen Poor metabolizers (CYP2C9*3/*3) should administrated with caution to avoid adverse cardiovascular and gastrointestinal events 
CYP2C9+VKORC1 Warfarina Dose adjustment based on CYP2C9 and VKORC1 genotypes to achieve efficacy and avoid bleeding complications 
CYP2C19 Clopidogrela Poor metabolizers (CYP2C19*2/*2) should take alternative therapy to avoid bleeding complications 
CYP2D6 Codeinea Ultra-rapid metabolizers (CYP2D6*1/*1 and *1/*2) should avoid usage due to potential for toxicity 
Doxepin Poor metabolizers (CYP2D6*3/*3) should reduce dose by 60% to avoid arrhythmia and myelosuppression 
DPD deficiency Capecitabine Avoid usage in DPD deficient patients to prevent severe ADRs 
Fluorouracila 
G6PD deficiency Chloroquine Avoid usage in G6PD deficient patients to prevent hemolysis 
Dapsone 
Rasburicase 
HLA-B*1502 Carbamazepinea Avoid usage in HLA-B*1502 carriers to prevent SJS/TEN 
Phenytoin 
HLA-B*5701 Abacavira Avoid usage in HLA-B*5701 carriers to prevent hepatotoxicity 
Flucloxacillin 
HLA-B*5801 Allopurinol Avoid usage in HLA-B*5801 carriers to prevent severe cutaneous ADRs 
SLCO1B1 Simvastatina Dose adjustment based on SLCO1B1 genotype (C allele of rs4149056 SLCO1B1) to avoid myopathy 
TPMT Azathioprinea Dose adjustment based on TPMT genotype to achieve efficacy and avoid bone-marrow suppression (non-functional alleles TPMT*2, TPMT*3A, and TPMT*3C) 
Mercaptopurinea 
UGT1A1 Irinotecana Dose adjustment based on UGT1A1 genotype (UGT1A1*28) to achieve efficacy and avoid neutropenia 

ADR, adverse drug reaction; CYP, cytochrome P450; DPD, dihydropyrimidine dehydrogenase; G6PD, glucose-6-phosphate dehydrogenase; HLA, human-leukocyte antigen; SJS, Stevens–Johnson syndrome; SLCO1B1, solute carrier organic anion transporter family, member 1B1; TEN, toxic epidermal necrolysis; TPMT, thiopurine S-methyltransferase; UGT, UDP-glucuronosyltransferase.

aGuidelines provided.

CHALLENGES AND FUTURE DIRECTIONS

It is well recognized that genetics can affect clinical outcomes of drug therapy. The greatest obstacle to the clinical implementation of genetic biomarker tests is that, with the exception of those listed in Table 2, few of them have sufficient sensitivity, specificity and predictive value to be clinically useful as screening tools to predict drug efficacy and prevent ADRs. This is especially true for the genes responsible for drug efficacy, as thus far pharmacogenomic studies on the efficacy of drugs used to treat common diseases have been disappointing. Taking statins again as an example, there is large variability in the clinical response to statin treatment. Genetic variants in HMGCR and APOE have been reported to influence the lipid-lowering response after stain therapy (52,53). However, conflicting results have also been reported for both APOE and for HMGCR (54,55). GWAS so far have identified multiple loci; however, each locus plays only a small role and none of the loci, alone or in combination, has shown clinical utility.

There are several reasons for the slow progress of the pharmacogenomic study of drug efficacy for common diseases. First, the causes of common diseases are multifactorial, involving both genetic and environmental factors, and in most cases genetic determinants underlying the disease pathogenesis are unknown. Thus, drugs used to treat these common diseases, such as statins, may target only one of the factors/pathways. If the cause of elevated blood lipid levels for an individual is not targeted by a statin, a statin would be ineffective. To better understand the mechanisms of drug efficacy and identify clinically useful biomarkers requires a better understanding of the diseases. Secondly, the effects of many drugs are influenced by drug–drug or drug–diet interactions. Drug efficacy may be modulated by concomitant drugs or diet, making it difficult to control pharmacogenomic studies. Similarly, common diseases are also largely influenced by both environment and diet. If life style and diet are not modified during statin treatment, the treatment may be of limited benefit for the patient (56). Obviously, more basic research is needed. It is hoped that a comprehensive study and analyses of combined data from GWAS, next generation sequencing, epigenetics, proteomics and metabolomics, and a detailed description of clinical phenotypes/endophenotypes as well as environmental factors will reveal functional variants not only for common diseases, but also for drug responses.

Even with the well-defined genetic variants (Table 2) that have been validated and shown to have high predictive value with robust clinical evidence of utility, broad acceptance by the medical community can be slow. Objective practice guidelines need to be developed. The regulation of gene tests and how test results can be incorporated preemptively into electronic medical record systems and, finally, issues related to the cost-effectiveness of testing also need to be addressed.

In conclusion, pharmacogenomics can play an important role in identifying responders and non-responders to medications, avoiding ADRs, and optimizing drug dosing, thus allowing for personalized therapy. Pharmacogenomics can also help reveal pathogenic mechanisms of disease. The clinically useful pharmacogenomic tests currently available are directed more at predicting drug toxicities and dose adjustment. More research will be needed to identifying genetic determinants of responders and non-responders, especially for drugs used to treat common complex diseases.

Conflict of Interest statement. Y.-T.C. is an inventor of ‘Risk Assessment for Adverse Drug Reactions’ which has been licensed to PharmiGene, Inc. Y.-T.C. Chairs the Scientific Advisory Board of PharmiGene, Inc.

FUNDING

This research was supported by grants from Academia Sinica, Taiwan (40-05-GMM) and National Science Council, Taiwan (NSC 101-2319-B-001-001, NSC 101-2325-B-001-006 and NSC 101-2325-B-001-035).

REFERENCES

1
Evans
W.E.
Relling
M.V.
Pharmacogenomics: translating functional genomics into rational therapeutics
Science
 , 
1999
, vol. 
286
 (pg. 
487
-
491
)
2
Meyer
U.A.
Pharmacogenetics—five decades of therapeutic lessons from genetic diversity
Nat. Rev. Genet.
 , 
2004
, vol. 
5
 (pg. 
669
-
676
)
3
Savonarola
A.
Palmirotta
R.
Guadagni
F.
Silvestris
F.
Pharmacogenetics and pharmacogenomics: role of mutational analysis in anti-cancer targeted therapy
Pharmacogenomics J
 , 
2012
, vol. 
12
 (pg. 
277
-
286
)
4
Ong
F.S.
Deignan
J.L.
Kuo
J.Z.
Bernstein
K.E.
Rotter
J.I.
Grody
W.W.
Das
K.
Clinical utility of pharmacogenetic biomarkers in cardiovascular therapeutics: a challenge for clinical implementation
Pharmacogenomics
 , 
2012
, vol. 
13
 (pg. 
465
-
475
)
5
Lazarou
J.
Pomeranz
B.H.
Corey
P.N.
Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies
JAMA
 , 
1998
, vol. 
279
 (pg. 
1200
-
1205
)
6
Severino
G.
Del Zompo
M.
Adverse drug reactions: role of pharmacogenomics
Pharmacol. Res.
 , 
2004
, vol. 
49
 (pg. 
363
-
373
)
7
Need
A.C.
Motulsky
A.G.
Goldstein
D.B.
Priorities and standards in pharmacogenetic research
Nat. Genet.
 , 
2005
, vol. 
37
 (pg. 
671
-
681
)
8
Pirmohamed
M.
Naisbitt
D.J.
Gordon
F.
Park
B.K.
The danger hypothesis—potential role in idiosyncratic drug reactions
Toxicology
 , 
2002
, vol. 
181–182
 (pg. 
55
-
63
)
9
Pirmohamed
M.
Park
B.K.
Genetic susceptibility to adverse drug reactions
Trends Pharmacol. Sci.
 , 
2001
, vol. 
22
 (pg. 
298
-
305
)
10
Amur
S.
Frueh
F.W.
Lesko
L.J.
Huang
S.M.
Integration and use of biomarkers in drug development, regulation and clinical practice: a US regulatory perspective
Biomark. Med.
 , 
2008
, vol. 
2
 (pg. 
305
-
311
)
11
Ingelman-Sundberg
M.
Oscarson
M.
McLellan
R.A.
Polymorphic human cytochrome P450 enzymes: an opportunity for individualized drug treatment
Trends Pharmacol. Sci.
 , 
1999
, vol. 
20
 (pg. 
342
-
349
)
12
Kirchheiner
J.
Henckel
H.B.
Franke
L.
Meineke
I.
Tzvetkov
M.
Uebelhack
R.
Roots
I.
Brockmoller
J.
Impact of the CYP2D6 ultra-rapid metabolizer genotype on doxepin pharmacokinetics and serotonin in platelets
Pharmacogenet. Genomics
 , 
2005
, vol. 
15
 (pg. 
579
-
587
)
13
Kirchheiner
J.
Nickchen
K.
Bauer
M.
Wong
M.L.
Licinio
J.
Roots
I.
Brockmoller
J.
Pharmacogenetics of antidepressants and antipsychotics: the contribution of allelic variations to the phenotype of drug response
Mol. Psychiatry
 , 
2004
, vol. 
9
 (pg. 
442
-
473
)
14
Crews
K.R.
Gaedigk
A.
Dunnenberger
H.M.
Klein
T.E.
Shen
D.D.
Callaghan
J.T.
Kharasch
E.D.
Skaar
T.C.
Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for codeine therapy in the context of cytochrome P450 2D6 (CYP2D6) genotype
Clin. Pharmacol. Ther.
 , 
2012
, vol. 
91
 (pg. 
321
-
326
)
15
Mega
J.L.
Simon
T.
Collet
J.P.
Anderson
J.L.
Antman
E.M.
Bliden
K.
Cannon
C.P.
Danchin
N.
Giusti
B.
Gurbel
P.
, et al.  . 
Reduced-function CYP2C19 genotype and risk of adverse clinical outcomes among patients treated with clopidogrel predominantly for PCI: a meta-analysis
JAMA
 , 
2010
, vol. 
304
 (pg. 
1821
-
1830
)
16
Eichelbaum
M.
Ingelman-Sundberg
M.
Evans
W.E.
Pharmacogenomics and individualized drug therapy
Annu. Rev. Med.
 , 
2006
, vol. 
57
 (pg. 
119
-
137
)
17
Schaeffeler
E.
Fischer
C.
Brockmeier
D.
Wernet
D.
Moerike
K.
Eichelbaum
M.
Zanger
U.M.
Schwab
M.
Comprehensive analysis of thiopurine S-methyltransferase phenotype-genotype correlation in a large population of German-Caucasians and identification of novel TPMT variants
Pharmacogenetics
 , 
2004
, vol. 
14
 (pg. 
407
-
417
)
18
Ando
Y.
Saka
H.
Ando
M.
Sawa
T.
Muro
K.
Ueoka
H.
Yokoyama
A.
Saitoh
S.
Shimokata
K.
Hasegawa
Y.
Polymorphisms of UDP-glucuronosyltransferase gene and irinotecan toxicity: a pharmacogenetic analysis
Cancer Res.
 , 
2000
, vol. 
60
 (pg. 
6921
-
6926
)
19
Yuan
H.Y.
Chen
J.J.
Lee
M.T.
Wung
J.C.
Chen
Y.F.
Charng
M.J.
Lu
M.J.
Hung
C.R.
Wei
C.Y.
Chen
C.H.
, et al.  . 
A novel functional VKORC1 promoter polymorphism is associated with inter-individual and inter-ethnic differences in warfarin sensitivity
Hum. Mol. Genet.
 , 
2005
, vol. 
14
 (pg. 
1745
-
1751
)
20
Gullov
A.L.
Koefoed
B.G.
Petersen
P.
Bleeding complications to long-term oral anticoagulant therapy
J. Thromb. Thrombolysis
 , 
1994
, vol. 
1
 (pg. 
17
-
25
)
21
Cooper
G.M.
Johnson
J.A.
Langaee
T.Y.
Feng
H.
Stanaway
I.B.
Schwarz
U.I.
Ritchie
M.D.
Stein
C.M.
Roden
D.M.
Smith
J.D.
, et al.  . 
A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose
Blood
 , 
2008
, vol. 
112
 (pg. 
1022
-
1027
)
22
Takahashi
H.
Echizen
H.
Pharmacogenetics of warfarin elimination and its clinical implications
Clin. Pharmacokinet.
 , 
2001
, vol. 
40
 (pg. 
587
-
603
)
23
Bell
R.G.
Matschiner
J.T.
Warfarin and the inhibition of vitamin K activity by an oxide metabolite
Nature
 , 
1972
, vol. 
237
 (pg. 
32
-
33
)
24
Takeuchi
F.
McGinnis
R.
Bourgeois
S.
Barnes
C.
Eriksson
N.
Soranzo
N.
Whittaker
P.
Ranganath
V.
Kumanduri
V.
McLaren
W.
, et al.  . 
A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose
PLoS Genet.
 , 
2009
, vol. 
5
 pg. 
e1000433
 
25
Klein
T.E.
Altman
R.B.
Eriksson
N.
Gage
B.F.
Kimmel
S.E.
Lee
M.T.
Limdi
N.A.
Page
D.
Roden
D.M.
Wagner
M.J.
, et al.  . 
Estimation of the warfarin dose with clinical and pharmacogenetic data
N. Engl. J. Med.
 , 
2009
, vol. 
360
 (pg. 
753
-
764
)
26
Limdi
N.A.
Wadelius
M.
Cavallari
L.
Eriksson
N.
Crawford
D.C.
Lee
M.T.
Chen
C.H.
Motsinger-Reif
A.
Sagreiya
H.
Liu
N.
, et al.  . 
Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups
Blood
 , 
2010
, vol. 
115
 (pg. 
3827
-
3834
)
27
Gonzalez
F.J.
Fernandez-Salguero
P.
Diagnostic analysis, clinical importance and molecular basis of dihydropyrimidine dehydrogenase deficiency
Trends Pharmacol. Sci.
 , 
1995
, vol. 
16
 (pg. 
325
-
327
)
28
van Kuilenburg
A.B.
Screening for dihydropyrimidine dehydrogenase deficiency: to do or not to do, that's the question
Cancer Invest.
 , 
2006
, vol. 
24
 (pg. 
215
-
217
)
29
Giacomini
K.M.
Huang
S.M.
Tweedie
D.J.
Benet
L.Z.
Brouwer
K.L.
Chu
X.
Dahlin
A.
Evers
R.
Fischer
V.
Hillgren
K.M.
, et al.  . 
Membrane transporters in drug development
Nat. Rev. Drug Discov.
 , 
2010
, vol. 
9
 (pg. 
215
-
236
)
30
Hoffmeyer
S.
Burk
O.
von Richter
O.
Arnold
H.P.
Brockmoller
J.
Johne
A.
Cascorbi
I.
Gerloff
T.
Roots
I.
Eichelbaum
M.
, et al.  . 
Functional polymorphisms of the human multidrug-resistance gene: multiple sequence variations and correlation of one allele with P-glycoprotein expression and activity in vivo
Proc. Natl Acad. Sci. USA
 , 
2000
, vol. 
97
 (pg. 
3473
-
3478
)
31
Hauser
I.A.
Schaeffeler
E.
Gauer
S.
Scheuermann
E.H.
Wegner
B.
Gossmann
J.
Ackermann
H.
Seidl
C.
Hocher
B.
Zanger
U.M.
, et al.  . 
ABCB1 genotype of the donor but not of the recipient is a major risk factor for cyclosporine-related nephrotoxicity after renal transplantation
J. Am. Soc. Nephrol.
 , 
2005
, vol. 
16
 (pg. 
1501
-
1511
)
32
Cusatis
G.
Gregorc
V.
Li
J.
Spreafico
A.
Ingersoll
R.G.
Verweij
J.
Ludovini
V.
Villa
E.
Hidalgo
M.
Sparreboom
A.
, et al.  . 
Pharmacogenetics of ABCG2 and adverse reactions to gefitinib
J. Natl Cancer Inst.
 , 
2006
, vol. 
98
 (pg. 
1739
-
1742
)
33
Ballantyne
C.M.
Achieving greater reductions in cardiovascular risk: lessons from statin therapy on risk measures and risk reduction
Am. Heart J.
 , 
2004
, vol. 
148
 (pg. 
S3
-
S8
)
34
Link
E.
Parish
S.
Armitage
J.
Bowman
L.
Heath
S.
Matsuda
F.
Gut
I.
Lathrop
M.
Collins
R.
SLCO1B1 variants and statin-induced myopathy—a genomewide study
N. Engl. J. Med.
 , 
2008
, vol. 
359
 (pg. 
789
-
799
)
35
Konig
J.
Seithel
A.
Gradhand
U.
Fromm
M.F.
Pharmacogenomics of human OATP transporters
Naunyn. Schmiedebergs Arch. Pharmacol.
 , 
2006
, vol. 
372
 (pg. 
432
-
443
)
36
Voora
D.
Shah
S.H.
Spasojevic
I.
Ali
S.
Reed
C.R.
Salisbury
B.A.
Ginsburg
G.S.
The SLCO1B1*5 genetic variant is associated with statin-induced side effects
J. Am. Coll. Cardiol.
 , 
2009
, vol. 
54
 (pg. 
1609
-
1616
)
37
Puccetti
L.
Ciani
F.
Auteri
A.
Genetic involvement in statins induced myopathy. Preliminary data from an observational case-control study
Atherosclerosis
 , 
2010
, vol. 
211
 (pg. 
28
-
29
)
38
Wilke
R.A.
Ramsey
L.B.
Johnson
S.G.
Maxwell
W.D.
McLeod
H.L.
Voora
D.
Krauss
R.M.
Roden
D.M.
Feng
Q.
Cooper-Dehoff
R.M.
, et al.  . 
The Clinical Pharmacogenomics Implementation Consortium: CPIC Guideline for SLCO1B1 and Simvastatin-Induced Myopathy
Clin. Pharmacol. Ther.
 , 
2012
, vol. 
92
 (pg. 
112
-
117
)
39
Hung
S.I.
Chung
W.H.
Jee
S.H.
Chen
W.C.
Chang
Y.T.
Lee
W.R.
Hu
S.L.
Wu
M.T.
Chen
G.S.
Wong
T.W.
, et al.  . 
Genetic susceptibility to carbamazepine-induced cutaneous adverse drug reactions
Pharmacogenet. Genomics
 , 
2006
, vol. 
16
 (pg. 
297
-
306
)
40
Kim
S.H.
Choi
J.H.
Lee
K.W.
Shin
E.S.
Oh
H.B.
Suh
C.H.
Nahm
D.H.
Park
H.S.
The human leucocyte antigen-DRB1*1302-DQB1*0609-DPB1*0201 haplotype may be a strong genetic marker for aspirin-induced urticaria
Clin. Exp. Allergy
 , 
2005
, vol. 
35
 (pg. 
339
-
344
)
41
Kim
S.H.
Ye
Y.M.
Lee
S.K.
Park
H.S.
Genetic mechanism of aspirin-induced urticaria/angioedema
Curr. Opin. Allergy Clin. Immunol.
 , 
2006
, vol. 
6
 (pg. 
266
-
270
)
42
Ozeki
T.
Mushiroda
T.
Yowang
A.
Takahashi
A.
Kubo
M.
Shirakata
Y.
Ikezawa
Z.
Iijima
M.
Shiohara
T.
Hashimoto
K.
, et al.  . 
Genome-wide association study identifies HLA-A*3101 allele as a genetic risk factor for carbamazepine-induced cutaneous adverse drug reactions in Japanese population
Hum. Mol. Genet.
 , 
2010
, vol. 
20
 (pg. 
1034
-
1041
)
43
McCormack
M.
Alfirevic
A.
Bourgeois
S.
Farrell
J.J.
Kasperaviciute
D.
Carrington
M.
Sills
G.J.
Marson
T.
Jia
X.
de Bakker
P.I.
, et al.  . 
HLA-A*3101 and carbamazepine-induced hypersensitivity reactions in Europeans
N. Engl. J. Med.
 , 
2011
, vol. 
364
 (pg. 
1134
-
1143
)
44
Hughes
A.R.
Mosteller
M.
Bansal
A.T.
Davies
K.
Haneline
S.A.
Lai
E.H.
Nangle
K.
Scott
T.
Spreen
W.R.
Warren
L.L.
, et al.  . 
Association of genetic variations in HLA-B region with hypersensitivity to abacavir in some, but not all, populations
Pharmacogenomics
 , 
2004
, vol. 
5
 (pg. 
203
-
211
)
45
Rudolph
M.G.
Stanfield
R.L.
Wilson
I.A.
How TCRs bind MHCs, peptides, and coreceptors
Annu. Rev. Immunol.
 , 
2006
, vol. 
24
 (pg. 
419
-
466
)
46
Nassif
A.
Bensussan
A.
Boumsell
L.
Deniaud
A.
Moslehi
H.
Wolkenstein
P.
Bagot
M.
Roujeau
J.C.
Toxic epidermal necrolysis: effector cells are drug-specific cytotoxic T cells
J. Allergy Clin. Immunol.
 , 
2004
, vol. 
114
 (pg. 
1209
-
1215
)
47
Pichler
W.J.
Delayed drug hypersensitivity reactions
Ann. Intern. Med.
 , 
2003
, vol. 
139
 (pg. 
683
-
693
)
48
Padovan
E.
Bauer
T.
Tongio
M.M.
Kalbacher
H.
Weltzien
H.U.
Penicilloyl peptides are recognized as T cell antigenic determinants in penicillin allergy
Eur. J. Immunol.
 , 
1997
, vol. 
27
 (pg. 
1303
-
1307
)
49
Pichler
W.J.
Beeler
A.
Keller
M.
Lerch
M.
Posadas
S.
Schmid
D.
Spanou
Z.
Zawodniak
A.
Gerber
B.
Pharmacological interaction of drugs with immune receptors: the p-i concept
Allergol. Int.
 , 
2006
, vol. 
55
 (pg. 
17
-
25
)
50
Wei
C.Y.
Chung
W.H.
Huang
H.W.
Chen
Y.T.
Hung
S.I.
Direct interaction between HLA-B and carbamazepine activates T cells in patients with Stevens-Johnson syndrome
J. Allergy Clin. Immunol
 , 
2012
, vol. 
129
 (pg. 
1562
-
1569
)
51
Ou Yang
C.W.
Hung
S.I.
Juo
C.G.
Lin
Y.P.
Fang
W.H.
Lu
I.H.
Chen
S.T.
Chen
Y.T.
HLA-B*1502-bound peptides: implications for the pathogenesis of carbamazepine-induced Stevens-Johnson syndrome
J. Allergy Clin. Immunol.
 , 
2007
, vol. 
120
 (pg. 
870
-
877
)
52
Chasman
D.I.
Posada
D.
Subrahmanyan
L.
Cook
N.R.
Stanton
V.P.
Jr
Ridker
P.M.
Pharmacogenetic study of statin therapy and cholesterol reduction
JAMA
 , 
2004
, vol. 
291
 (pg. 
2821
-
2827
)
53
Nieminen
T.
Kahonen
M.
Viiri
L.E.
Gronroos
P.
Lehtimaki
T.
Pharmacogenetics of apolipoprotein E gene during lipid-lowering therapy: lipid levels and prevention of coronary heart disease
Pharmacogenomics
 , 
2008
, vol. 
9
 (pg. 
1475
-
1486
)
54
Zintzaras
E.
Kitsios
G.D.
Triposkiadis
F.
Lau
J.
Raman
G.
APOE gene polymorphisms and response to statin therapy
Pharmacogenomics J.
 , 
2009
, vol. 
9
 (pg. 
248
-
257
)
55
Voora
D.
Shah
S.H.
Reed
C.R.
Zhai
J.
Crosslin
D.R.
Messer
C.
Salisbury
B.A.
Ginsburg
G.S.
Pharmacogenetic predictors of statin-mediated low-density lipoprotein cholesterol reduction and dose response
Circ. Cardiovasc. Genet.
 , 
2008
, vol. 
1
 (pg. 
100
-
106
)
56
Superko
H.R.
Momary
K.M.
Li
Y.
Statins personalized
Med. Clin. North Am.
 , 
2012
, vol. 
96
 (pg. 
123
-
139
)
57
Mallal
S.
Nolan
D.
Witt
C.
Masel
G.
Martin
A.M.
Moore
C.
Sayer
D.
Castley
A.
Mamotte
C.
Maxwell
D.
, et al.  . 
Association between presence of HLA-B*5701, HLA-DR7, and HLA-DQ3 and hypersensitivity to HIV-1 reverse-transcriptase inhibitor abacavir
Lancet
 , 
2002
, vol. 
359
 (pg. 
727
-
732
)
58
Hung
S.I.
Chung
W.H.
Liou
L.B.
Chu
C.C.
Lin
M.
Huang
H.P.
Lin
Y.L.
Lan
J.L.
Yang
L.C.
Hong
H.S.
, et al.  . 
HLA-B*5801 allele as a genetic marker for severe cutaneous adverse reactions caused by allopurinol
Proc. Natl Acad. Sci. USA
 , 
2005
, vol. 
102
 (pg. 
4134
-
4139
)
59
Kaniwa
N.
Saito
Y.
Aihara
M.
Matsunaga
K.
Tohkin
M.
Kurose
K.
Sawada
J.
Furuya
H.
Takahashi
Y.
Muramatsu
M.
, et al.  . 
HLA-B locus in Japanese patients with anti-epileptics and allopurinol-related Stevens-Johnson syndrome and toxic epidermal necrolysis
Pharmacogenomics
 , 
2008
, vol. 
9
 (pg. 
1617
-
1622
)
60
Lonjou
C.
Borot
N.
Sekula
P.
Ledger
N.
Thomas
L.
Halevy
S.
Naldi
L.
Bouwes-Bavinck
J.N.
Sidoroff
A.
de Toma
C.
, et al.  . 
A European study of HLA-B in Stevens-Johnson syndrome and toxic epidermal necrolysis related to five high-risk drugs
Pharmacogenet. Genomics
 , 
2008
, vol. 
18
 (pg. 
99
-
107
)
61
Romano
A.
De Santis
A.
Romito
A.
Di Fonso
M.
Venuti
A.
Gasbarrini
G.B.
Manna
R.
Delayed hypersensitivity to aminopenicillins is related to major histocompatibility complex genes
Ann. Allergy Asthma Immunol.
 , 
1998
, vol. 
80
 (pg. 
433
-
437
)
62
Lucena
M.I.
Molokhia
M.
Shen
Y.
Urban
T.J.
Aithal
G.P.
Andrade
R.J.
Day
C.P.
Ruiz-Cabello
F.
Donaldson
P.T.
Stephens
C.
, et al.  . 
Susceptibility to amoxicillin-clavulanate-induced liver injury is influenced by multiple HLA class I and II alleles
Gastroenterology
 , 
2011
, vol. 
141
 (pg. 
338
-
347
)
63
Chung
W.H.
Hung
S.I.
Hong
H.S.
Hsih
M.S.
Yang
L.C.
Ho
H.C.
Wu
J.Y.
Chen
Y.T.
Medical genetics: a marker for Stevens-Johnson syndrome
Nature
 , 
2004
, vol. 
428
 pg. 
486
 
64
Dettling
M.
Cascorbi
I.
Opgen-Rhein
C.
Schaub
R.
Clozapine-induced agranulocytosis in schizophrenic Caucasians: confirming clues for associations with human leukocyte class I and II antigens
Pharmacogenomics J.
 , 
2007
, vol. 
7
 (pg. 
325
-
332
)
65
Daly
A.K.
Donaldson
P.T.
Bhatnagar
P.
Shen
Y.
Pe'er
I.
Floratos
A.
Daly
M.J.
Goldstein
D.B.
John
S.
Nelson
M.R.
, et al.  . 
HLA-B*5701 genotype is a major determinant of drug-induced liver injury due to flucloxacillin
Nat. Genet.
 , 
2009
, vol. 
41
 (pg. 
816
-
819
)
66
Singer
J.B.
Lewitzky
S.
Leroy
E.
Yang
F.
Zhao
X.
Klickstein
L.
Wright
T.M.
Meyer
J.
Paulding
C.A.
A genome-wide study identifies HLA alleles associated with lumiracoxib-related liver injury
Nat. Genet.
 , 
2010
, vol. 
42
 (pg. 
711
-
714
)
67
Kim
S.H.
Kim
M.
Lee
K.W.
Kang
H.R.
Park
H.W.
Jee
Y.K.
HLA-B*5901 is strongly associated with methazolamide-induced Stevens-Johnson syndrome/toxic epidermal necrolysis
Pharmacogenomics
 , 
2010
, vol. 
11
 (pg. 
879
-
884
)
68
Littera
R.
Carcassi
C.
Masala
A.
Piano
P.
Serra
P.
Ortu
F.
Corso
N.
Casula
B.
La Nasa
G.
Contu
L.
, et al.  . 
HLA-dependent hypersensitivity to nevirapine in Sardinian HIV patients
AIDS
 , 
2006
, vol. 
20
 (pg. 
1621
-
1626
)
69
Gatanaga
H.
Yazaki
H.
Tanuma
J.
Honda
M.
Genka
I.
Teruya
K.
Tachikawa
N.
Kikuchi
Y.
Oka
S.
HLA-Cw8 primarily associated with hypersensitivity to nevirapine
AIDS
 , 
2007
, vol. 
21
 (pg. 
264
-
265
)
70
Chantarangsu
S.
Mushiroda
T.
Mahasirimongkol
S.
Kiertiburanakul
S.
Sungkanuparph
S.
Manosuthi
W.
Tantisiriwat
W.
Charoenyingwattana
A.
Sura
T.
Chantratita
W.
, et al.  . 
HLA-B*3505 allele is a strong predictor for nevirapine-induced skin adverse drug reactions in HIV-infected Thai patients
Pharmacogenet. Genomics
 , 
2009
, vol. 
19
 (pg. 
139
-
146
)
71
Martin
A.M.
Nolan
D.
James
I.
Cameron
P.
Keller
J.
Moore
C.
Phillips
E.
Christiansen
F.T.
Mallal
S.
Predisposition to nevirapine hypersensitivity associated with HLA-DRB1*0101 and abrogated by low CD4 T-cell counts
AIDS
 , 
2005
, vol. 
19
 (pg. 
97
-
99
)
72
Locharernkul
C.
Loplumlert
J.
Limotai
C.
Korkij
W.
Desudchit
T.
Tongkobpetch
S.
Kangwanshiratada
O.
Hirankarn
N.
Suphapeetiporn
K.
Shotelersuk
V.
Carbamazepine and phenytoin induced Stevens-Johnson syndrome is associated with HLA-B*1502 allele in Thai population
Epilepsia
 , 
2008
, vol. 
49
 (pg. 
2087
-
2091
)
73
Kindmark
A.
Jawaid
A.
Harbron
C.G.
Barratt
B.J.
Bengtsson
O.F.
Andersson
T.B.
Carlsson
S.
Cederbrant
K.E.
Gibson
N.J.
Armstrong
M.
, et al.  . 
Genome-wide pharmacogenetic investigation of a hepatic adverse event without clinical signs of immunopathology suggests an underlying immune pathogenesis
Pharmacogenomics J.
 , 
2008
, vol. 
8
 (pg. 
186
-
195
)